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An improved technique for increasing the accuracy of joint-to-ground distance tracking in kinect v2 for foot-off and foot contact detection.

Amin AminiKonstantinos Banitsas
Published in: Journal of medical engineering & technology (2019)
The Kinect sensor has been widely used in different applications such as rehabilitation and gait analysis. Whilst Kinect v2 was released with several improvements over its predecessor, it still incorporates depth-map intrinsic inaccuracies. This results in inconsistencies in skeletal-data acquisition, especially in joint localisation and distance-to-ground tracking with respect to the Kinect's 3-D Cartesian coordinate reference point. This research proposes a correction technique based on the two-point linear equation derived from the information gathered from different subjects' skeletal data and data regression analysis to compensate the inaccuracies in joint-to-ground data collection. The research also proposes a new footsteps detection method based on skeletal data and plane detection techniques that calculates a footstep by using the ankle's Euclidean distance from the floor, regardless of the subject's distance from the camera. The results show that after the correction technique was applied, data acquisition proved to be consistent and more accurate within a distance range of 1.6-2.9 m from the Kinect camera, regardless of the subject's location to the camera's reference point. Moreover, the inconsistency of joint data read by the Kinect was reduced from 25.69% to 5.25% and the footsteps detection accuracy increased from 42.85% to 79.76% on average for both legs.
Keyphrases
  • electronic health record
  • big data
  • healthcare
  • social media
  • quantum dots